Combined classical/quantum predictor evaluation

ABSTRACT

Using a classical data model executing on a classical processor, a set of classical features is scored. A classical feature comprises a first attribute of a resource, and a score of the classical feature comprises an evaluation of a utility of the classical feature in predicting a result involving the resource. Using a quantum data model executing on a quantum processor and the scored set of classical features, a set of quantum features is scored. The scored set of classical features and the scored set of quantum features are correlated, forming a combined set of scored features. Using the combined set of scored features and a first set of input data of a resource, a valuation of the resource is calculated.

BACKGROUND

The present invention relates generally to a method, system, andcomputer program product for data attribute evaluation. Moreparticularly, the present invention relates to a method, system, andcomputer program product for combined classical/quantum predictorevaluation.

A predictor variable, or predictor, is a variable used to estimate orforecast a future event or outcome. For example, when evaluatingcomputing resource configurations for deployment within a data center,example predictors might be the number of processors in each system, thespeed of each processor, the network bandwidth available to eachcomputing resource, and the amount of storage available to eachcomputing resource, and the forecast outcome might be a data throughputof the entire data center once a particular resource configuration hasbeen deployed. Data modelling often includes analyzing one or morepredictors, as well as the relative weights of each predictor, todetermine the strength and direction of a predictor's association with aparticular outcome or criterion. Predictor valuation refers to valuing apredictor's ability to predict a desired outcome.

Hereinafter, a “Q” prefix in a word of phrase is indicative of areference of that word or phrase in a quantum computing context unlessexpressly distinguished where used.

Nature—including molecules—follows the laws of quantum mechanics, abranch of physics that explores how the physical world works at the mostfundamental levels. At this level, particles behave in strange ways,taking on more than one state at the same time, and interacting withother particles that are very far away. Quantum computing harnessesthese quantum phenomena to process information.

The computers we use today are known as classical computers (alsoreferred to herein as “conventional” computers or conventional nodes, or“CN”). A conventional computer uses a processor fabricated usingsemiconductor technology, a semiconductor memory, and a magnetic orsolid-state storage device, in what is known as a Von Neumannarchitecture. Particularly, the processors in conventional computers arebinary processors, i.e., operating on binary data represented in 1 and0.

A quantum processor (q-processor) uses the odd nature of entangled qubitdevices (compactly referred to herein as “qubit,” plural “qubits) toperform computational tasks. In the particular realms where quantummechanics operates, particles of matter can exist in multiplestates—such as an “on” state, an “off” state, and both “on” and “off”states simultaneously. Where binary computing using semiconductorprocessors is limited to using just the on and off states (equivalent to1 and 0 in binary code), a quantum processor harnesses these quantumstates of matter to output signals that are usable in data computing.

Conventional computers encode information in bits. Each bit can take thevalue of 1 or 0. These 1s and 0s act as on/off switches that ultimatelydrive computer functions. Quantum computers, on the other hand, arebased on qubits, which operate according to two key principles ofquantum physics: superposition and entanglement. Superposition meansthat each qubit can represent both a 1 and a 0 at the same time.Entanglement means that qubits in a superposition can be correlated witheach other in a non-classical way; that is, the state of one (whether itis a 1 or a 0) can depend on the state of another, and that there ismore information that can be ascertained about the two qubits when theyare entangled than when they are treated individually. Using these twoprinciples, qubits operate as more sophisticated processors ofinformation, enabling quantum computers to function in ways that allowthem to solve difficult problems that are intractable using conventionalcomputers. IBM has successfully constructed and demonstrated theoperability of a quantum processor (IBM is a registered trademark ofInternational Business Machines corporation in the United States and inother countries.)

SUMMARY

The illustrative embodiments provide a method, system, and computerprogram product. An embodiment includes a method that scores, using aclassical data model executing on a classical processor, a set ofclassical features, a classical feature in the set of classical featurescomprising a first attribute of a resource, a score of the classicalfeature comprising an evaluation of a utility of the classical featurein predicting a result involving the resource. An embodiment scores,using a quantum data model executing on a quantum processor and thescored set of classical features, a set of quantum features, a quantumfeature in the set of quantum features comprising a second attribute ofthe resource, a score of the quantum feature comprising an evaluation ofa utility of the quantum feature in predicting the result. An embodimentcorrelates, forming a combined set of scored features, the scored set ofclassical features and the scored set of quantum features. An embodimentcalculates, using the combined set of scored features and a first set ofinput data of a resource, a valuation of the resource.

An embodiment includes a computer usable program product. The computerusable program product includes one or more computer-readable storagedevices, and program instructions stored on at least one of the one ormore storage devices.

An embodiment includes a computer system. The computer system includesone or more processors, one or more computer-readable memories, and oneor more computer-readable storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofthe illustrative embodiments when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented;

FIG. 2 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 depicts a block diagram of an example configuration for combinedclassical/quantum predictor evaluation in accordance with anillustrative embodiment;

FIG. 4 depicts a block diagram of an example configuration for combinedclassical/quantum predictor evaluation in accordance with anillustrative embodiment;

FIG. 5 depicts a block diagram of an example configuration for combinedclassical/quantum predictor evaluation in accordance with anillustrative embodiment;

FIG. 6 depicts an example of combined classical/quantum predictorevaluation in accordance with an illustrative embodiment;

FIG. 7 depicts a flowchart of an example process for combinedclassical/quantum predictor evaluation in accordance with anillustrative embodiment;

FIG. 8 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 9 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The illustrative embodiments recognize that predictor valuation, andselection of the best (i.e. highest valued) predictors, determines theaccuracy of the outcome the predictors predict. However, classicalcomputing techniques for predictor valuation and selection, such asstatistical analysis, predictive modeling, or a combination of classicalapproaches are insufficiently accurate, insufficiently personalizable toa specific user need, and fast enough to analyze data at the scale,volume, and velocity needed today. Thus, the illustrative embodimentsrecognize that there is an unmet need for predictor valuation that ismore accurate and more quickly computable than the techniques currentlyavailable.

The illustrative embodiments recognize that the presently availabletools or solutions do not address these needs or provide adequatesolutions for these needs. The illustrative embodiments used to describethe invention generally address and solve the above-described problemsand other problems related to combined classical/quantum predictorevaluation.

An embodiment can be implemented as a software application. Theapplication implementing an embodiment can be configured as amodification of an existing predictor evaluation or data modellingsystem, as a separate application that operates in conjunction with anexisting predictor evaluation or data modelling system, a standaloneapplication, or some combination thereof

Particularly, some illustrative embodiments provide a method that scoresa set of classical features using a classical data model executing on aclassical processor, scores a set of quantum features using a quantumdata model executing on a quantum processor and the scored set ofclassical features, correlates, forming a combined set of scoredfeatures, the scored set of classical features and the scored set ofquantum features, and predicts, using the combined set of scoredfeatures and a first set of input data of a resource, a valuation of theresource.

An embodiment receives, as input, a set of data. In one embodiment, thedata comprises a set of attribute-value pairs, in which the attributelabels or describes the data value. A data attribute is also referred toas a feature. For example, one attribute-value pair might have processorspeed as the attribute and 2 GHz as the value. In another embodiment,the data comprises a stream, or set of values in order, labelled withthe attribute. One non-limiting example of a stream is the output of atemperature measurement system, labelled with “temperature” andincluding a set of temperature measurement results obtained at one-hourintervals. The data may be historical, may be data of a currentlyoccurring event, or a combination. An embodiment also receives, asinput, a set of outcome data associated with the input, for use in modeltraining and predictor evaluation. One or more data attributes arepotential predictors.

An embodiment uses a classical data model executing on a classicalprocessor to select and score a set of data attributes, or features,from input training data. One embodiment selects features to score usinga set of heuristics. Another embodiment selects features to score usinga set of analytical methods. Another embodiment selects features toscore using an automated artificial intelligence tool, such as AutoAI.As well as feature selection, an embodiment fills in missing data forthe selected features, scales feature data to a common scale, andperforms other data cleanup for selected features and their data.

An embodiment selects a model type with which to analyze the inputtraining data with respect to a feature being scored. One embodimentselects a model type based on a characteristic of the feature beingscored. For example, if there are only two data values for an attribute,the embodiment selects a binary classification type model. As anotherexample, if the attribute could have an unknowable set of possiblevalues, the embodiment selects a regression type model. An embodimentrecursively re-selects features and refines a selected model type andmodel weights and parameters until the model satisfies a completioncriterion, such as optimizing accuracy, by obtaining a set of featuresthat best predict a particular result. An embodiment uses a weight of afeature within the resulting model as the feature's score. Anotherembodiment selects and refines an ensemble of model types, adjusts eachmodel in the ensemble's weights and parameters until each modelsatisfies a completion criterion, and combines the resulting weightsfrom the different models into one score for a feature. The embodimentcombines the resulting weights using any presently knownweight-combining technique. Another embodiment scores features bymultiplying a feature's weight by the absolute value of the Pearsonscorrelation coefficient, a presently known technique that measures thestatistical relationship between two variables, providing informationabout the magnitude of the association between a predictor and a desiredresult. Another embodiment uses, instead of model weights, the output ofa model's explainer module, which produces a score corresponding to animportance of a feature in predicting a desired result. Anotherembodiment uses a presently known random forest technique to fit arandom forest to input training data and scores features according todata fit. Other techniques for scoring predictors using a classicalprocessor are also possible and contemplated within the scope of theillustrative embodiments.

An embodiment uses the feature scores to select a set of highest-scored,or best, features. One embodiment selects all features with scores abovea threshold value. Another embodiment ranks features by their scores,then selects a predetermined number of the top-ranked features, forexample the top ten or fifteen features. Another embodiment uses anotherfeature selection method.

An embodiment uses a quantum data model executing on a quantum processorto select and score another set of data attributes, or features, frominput training data. One embodiment starts with the top-ranked classicalfeatures (the features scored using the classical processor) andperforms a presently known recursive feature elimination with crossvalidation (RFECV) technique on a support vector machine executing on aquantum processor (QSVM) to select and score a set of quantum features.A support-vector machine is a presently known supervised learning modelwith one or more associated learning algorithms that analyze data forclassification and regression analysis. Another embodiment uses thetop-ranked classical features to perform a quadratic unconstrainedbinary optimization (QUBO) technique and executes a quantum approximateoptimization algorithm (QAOA) on a quantum processor to select and scorea set of quantum features. QAOA is a presently known heuristic techniquethat transforms a simple many-qubit wave function into one which encodesthe solution to an optimization problem. Both QSVM and QAOA include anexplainer module, which produces a score corresponding to an importanceof a feature in predicting a desired result. One embodiment applies thetop-ranked classical features as quadratic penalties to force QUBO tofind a different set of predictors as input to QAOA. Other techniquesfor scoring predictors using a quantum processor are also possible andcontemplated within the scope of the illustrative embodiments.

An embodiment uses the feature scores scored by a quantum processor (thequantum features) to select a set of highest-scored, or best, quantumfeatures. One embodiment selects all quantum features with scores abovea threshold value. Another embodiment ranks quantum features by theirscores, then selects a predetermined number of the top-ranked quantumfeatures, for example the top ten or fifteen features. Anotherembodiment uses another quantum feature selection method.

An embodiment correlates the scored set of classical features and thescored set of quantum features into a combined set of scored features.One embodiment selects a set of features that are common to both theclassical and quantum feature sets. Then, for a feature in the combinedset, an embodiment multiples the feature's classical score (the scoreoutput using the classical processor) with the feature's quantum score(the score output using the quantum processor), including a scalingfactor if necessary to normalize both scores to a common scale.

An embodiment uses the combined set of scored features and input data tocalculate a valuation of a resource or predict an output value. Oneembodiment calculates a valuation of a resource by using a product sumof feature value and weight. For example, to evaluate a computingresource when configuring a computing system deployment, the combinedset of scored features might indicate that processor speed and networkbandwidth are the most important features in determining a computingresource's throughput, and that valuation is proportional to bothprocessor speed and throughput. Thus, a resource having a processorspeed of 2 GHz and a network bandwidth of 100 megabits per second mightbe half as valuable than a resource having a processor speed of 4 GHzand a network bandwidth of 200 megabits per second, and the higher-speedresource should be selected for the deployment instead of thelower-speed resource. As another example, to predict a height of a cloudlayer above the ground, the combined set of scored features mightindicate that temperature, atmospheric pressure, and dew point (thetemperature below which water droplets begin to condense) are the mostimportant features in determining a height of a cloud layer above theground. Thus, given an atmospheric pressure of 1013 millibars, and atemperature and dew point of eight degrees Celsius each, an embodimentmight predict that the cloud layer is zero meters above the ground (inother words, fog is forming or about to form). Note that these exampleshave been extremely simplified only for ease of illustration, and do notrepresent the complexity of actual data analysis implementations.

One embodiment uses the combined set of scored features and a single setof input data to calculate a valuation of a resource or predict anoutput value. For example, input data for valuing a computing resourcemight include the resource's processor speed and network bandwidth.Another embodiment uses the combined set of scored features and a streamof input data to generate a stream of valuations or predictions. Forexample, a cloud height prediction system might receive a stream oftemperature, dew point, and pressure measurements, and output acorresponding stream of predictions. Time intervals of input and outputstreams need not be the same.

An embodiment uses a natural language processing system, the combinedset of scored features, and the valuation or prediction to construct anatural language explanation of the valuation or prediction expressed innatural language form. For example, for the example computing resourcesdescribed herein, an embodiment might construct a natural languageexplanation such as, “Because Resource A's processor speed of 2 GHz andnetwork bandwidth of 100 megabits per second are half as useful thanResource B's processor speed of 4 GHz and network bandwidth of 200megabits per second in the deployment you are configuring, I suggestdeploying Resource B instead of Resource A.” As another example, for theweather prediction described herein, an embodiment might construct anatural language explanation such as, “Because the temperature-dew pointspread is currently zero, cloud height is also predicted to be zerometers above ground and fog is likely to form within the next hour.”

Some predictors are better at predicting a short-term result than alonger-term result, and vice versa. For example, because cloud height isdetermined by current atmospheric conditions, the currenttemperature-dew point spread might be useful in predicting cloud heightfor the next hour or two, but unlikely to be as useful in predicting acloud height for a day next week or next month. As another example,transactions on a shopping website might exhibit daily (e.g. lessactivity at 3 am in a particular time zone than at 8 pm) and seasonal(e.g. more activity during December than in July) variations, so time ofday might be more useful in predicting the next hour's transaction load,and valuing a resource with which to handle that load, than nextDecember's. Thus, one embodiment uses a classical data model executingon a classical processor to select and score two or more sets offeatures from input training data, with each set of features using adifferent time horizon. The embodiment uses the sets of feature scoresto select a set of highest-scored, or best, features of each set foreach time horizon, and uses a quantum data model executing on a quantumprocessor to select and score another two or more sets of features frominput training data, with each set of quantum features using a differenttime horizon. For each time horizon, the embodiment correlates thescored sets of classical and quantum features into a combined set ofscored features. The embodiment uses the combined set of scored featuresand input data to calculate a valuation of a resource or predict anoutput value for a particular time horizon in a manner described herein.

The manner of combined classical/quantum predictor evaluation describedherein is unavailable in the presently available methods in thetechnological field of endeavor pertaining to data modelling andprediction. A method of an embodiment described herein, when implementedto execute on a device or data processing system, comprises substantialadvancement of the functionality of that device or data processingsystem in scoring a set of classical features using a classical datamodel executing on a classical processor, scoring a set of quantumfeatures using a quantum data model executing on a quantum processor andthe scored set of classical features, correlating, forming a combinedset of scored features, the scored set of classical features and thescored set of quantum features, and predicting, using the combined setof scored features and a first set of input data of a resource, avaluation of the resource.

The illustrative embodiments are described with respect to certain typesof input data, attributes, features, predictions, valuations,evaluations, models, forecasts, thresholds, rankings, adjustments,sensors, measurements, devices, data processing systems, environments,components, and applications only as examples. Any specificmanifestations of these and other similar artifacts are not intended tobe limiting to the invention. Any suitable manifestation of these andother similar artifacts can be selected within the scope of theillustrative embodiments.

Furthermore, the illustrative embodiments may be implemented withrespect to any type of data, data source, or access to a data sourceover a data network. Any type of data storage device may provide thedata to an embodiment of the invention, either locally at a dataprocessing system or over a data network, within the scope of theinvention. Where an embodiment is described using a mobile device, anytype of data storage device suitable for use with the mobile device mayprovide the data to such embodiment, either locally at the mobile deviceor over a data network, within the scope of the illustrativeembodiments.

The illustrative embodiments are described using specific code, designs,architectures, protocols, layouts, schematics, and tools only asexamples and are not limiting to the illustrative embodiments.Furthermore, the illustrative embodiments are described in someinstances using particular software, tools, and data processingenvironments only as an example for the clarity of the description. Theillustrative embodiments may be used in conjunction with othercomparable or similarly purposed structures, systems, applications, orarchitectures. For example, other comparable mobile devices, structures,systems, applications, or architectures therefor, may be used inconjunction with such embodiment of the invention within the scope ofthe invention. An illustrative embodiment may be implemented inhardware, software, or a combination thereof.

The examples in this disclosure are used only for the clarity of thedescription and are not limiting to the illustrative embodiments.Additional data, operations, actions, tasks, activities, andmanipulations will be conceivable from this disclosure and the same arecontemplated within the scope of the illustrative embodiments.

Any advantages listed herein are only examples and are not intended tobe limiting to the illustrative embodiments. Additional or differentadvantages may be realized by specific illustrative embodiments.Furthermore, a particular illustrative embodiment may have some, all, ornone of the advantages listed above.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

With reference to the figures and in particular with reference to FIGS.1 and 2 , these figures are example diagrams of data processingenvironments in which illustrative embodiments may be implemented. FIGS.1 and 2 are only examples and are not intended to assert or imply anylimitation with regard to the environments in which differentembodiments may be implemented. A particular implementation may makemany modifications to the depicted environments based on the followingdescription.

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented. Data processingenvironment 100 is a network of computers in which the illustrativeembodiments may be implemented. Data processing environment 100 includesnetwork 102. Network 102 is the medium used to provide communicationslinks between various devices and computers connected together withindata processing environment 100. Network 102 may include connections,such as wire, wireless communication links, or fiber optic cables.

Clients or servers are only example roles of certain data processingsystems connected to network 102 and are not intended to exclude otherconfigurations or roles for these data processing systems. Server 104and server 106 couple to network 102 along with storage unit 108.Software applications may execute on any computer in data processingenvironment 100. Clients 110, 112, and 114 are also coupled to network102. A data processing system, such as server 104 or 106, or client 110,112, or 114 may contain data and may have software applications orsoftware tools executing thereon.

Only as an example, and without implying any limitation to sucharchitecture, FIG. 1 depicts certain components that are usable in anexample implementation of an embodiment. For example, servers 104 and106, and clients 110, 112, 114, are depicted as servers and clients onlyas example and not to imply a limitation to a client-serverarchitecture. As another example, an embodiment can be distributedacross several data processing systems and a data network as shown,whereas another embodiment can be implemented on a single dataprocessing system within the scope of the illustrative embodiments. Dataprocessing systems 104, 106, 110, 112, and 114 also represent examplenodes in a cluster, partitions, and other configurations suitable forimplementing an embodiment.

Device 132 is an example of a device described herein. For example,device 132 can take the form of a smartphone, a tablet computer, alaptop computer, client 110 in a stationary or a portable form, awearable computing device, or any other suitable device. Any softwareapplication described as executing in another data processing system inFIG. 1 can be configured to execute in device 132 in a similar manner.Any data or information stored or produced in another data processingsystem in FIG. 1 can be configured to be stored or produced in device132 in a similar manner.

A quantum computing device 146 comprises one or more q-processors 148. Acurrently viable qubit is an example of q-processor 148. Quantumcomputing device 148 may couple to network 102 using wired connections,wireless communication protocols, or other suitable data connectivity.

Application 105 implements an embodiment described herein. Application105 executes partially in any of servers 104 and 106, clients 110, 112,and 114, and device 132, and partially in quantum computing device 148.

Servers 104 and 106, storage unit 108, and clients 110, 112, and 114,and device 132 may couple to network 102 using wired connections,wireless communication protocols, or other suitable data connectivity.Clients 110, 112, and 114 may be, for example, personal computers ornetwork computers.

In the depicted example, server 104 may provide data, such as bootfiles, operating system images, and applications to clients 110, 112,and 114. Clients 110, 112, and 114 may be clients to server 104 in thisexample. Clients 110, 112, 114, or some combination thereof, may includetheir own data, boot files, operating system images, and applications.Data processing environment 100 may include additional servers, clients,and other devices that are not shown.

In the depicted example, data processing environment 100 may be theInternet. Network 102 may represent a collection of networks andgateways that use the Transmission Control Protocol/Internet Protocol(TCP/IP) and other protocols to communicate with one another. At theheart of the Internet is a backbone of data communication links betweenmajor nodes or host computers, including thousands of commercial,governmental, educational, and other computer systems that route dataand messages. Of course, data processing environment 100 also may beimplemented as a number of different types of networks, such as forexample, an intranet, a local area network (LAN), or a wide area network(WAN). FIG. 1 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

Among other uses, data processing environment 100 may be used forimplementing a client-server environment in which the illustrativeembodiments may be implemented. A client-server environment enablessoftware applications and data to be distributed across a network suchthat an application functions by using the interactivity between aclient data processing system and a server data processing system. Dataprocessing environment 100 may also employ a service orientedarchitecture where interoperable software components distributed acrossa network may be packaged together as coherent business applications.Data processing environment 100 may also take the form of a cloud, andemploy a cloud computing model of service delivery for enablingconvenient, on-demand network access to a shared pool of configurablecomputing resources (e.g. networks, network bandwidth, servers,processing, memory, storage, applications, virtual machines, andservices) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service.

With reference to FIG. 2 , this figure depicts a block diagram of a dataprocessing system in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as servers104 and 106, or clients 110, 112, and 114 in FIG. 1 , or another type ofdevice in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

Data processing system 200 is also representative of a data processingsystem or a configuration therein, such as data processing system 132 inFIG. 1 in which computer usable program code or instructionsimplementing the processes of the illustrative embodiments may belocated. Data processing system 200 is described as a computer only asan example, without being limited thereto. Implementations in the formof other devices, such as device 132 in FIG. 1 , may modify dataprocessing system 200, such as by adding a touch interface, and eveneliminate certain depicted components from data processing system 200without departing from the general description of the operations andfunctions of data processing system 200 described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and memory controller hub (NB/MCH)202 and South Bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 arecoupled to North Bridge and memory controller hub (NB/MCH) 202.Processing unit 206 may contain one or more processors and may beimplemented using one or more heterogeneous processor systems.Processing unit 206 may be a multi-core processor. Graphics processor210 may be coupled to NB/MCH 202 through an accelerated graphics port(AGP) in certain implementations.

In the depicted example, local area network (LAN) adapter 212 is coupledto South Bridge and I/O controller hub (SB/ICH) 204. Audio adapter 216,keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224,universal serial bus (USB) and other ports 232, and PCl/PCIe devices 234are coupled to South Bridge and I/O controller hub 204 through bus 238.Hard disk drive (HDD) or solid-state drive (SSD) 226 and CD-ROM 230 arecoupled to South Bridge and I/O controller hub 204 through bus 240.PCI/PCIe devices 234 may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 226 and CD-ROM 230may use, for example, an integrated drive electronics (IDE), serialadvanced technology attachment (SATA) interface, or variants such asexternal-SATA (eSATA) and micro- SATA (mSATA). A super I/O (SIO) device236 may be coupled to South Bridge and I/O controller hub (SB/ICH) 204through bus 238.

Memories, such as main memory 208, ROM 224, or flash memory (not shown),are some examples of computer usable storage devices. Hard disk drive orsolid state drive 226, CD-ROM 230, and other similarly usable devicesare some examples of computer usable storage devices including acomputer usable storage medium.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within dataprocessing system 200 in FIG. 2 . The operating system may be acommercially available operating system for any type of computingplatform, including but not limited to server systems, personalcomputers, and mobile devices. An object oriented or other type ofprogramming system may operate in conjunction with the operating systemand provide calls to the operating system from programs or applicationsexecuting on data processing system 200.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs, such as application 105 in FIG. 1, are located on storage devices, such as in the form of code 226A onhard disk drive 226, and may be loaded into at least one of one or morememories, such as main memory 208, for execution by processing unit 206.The processes of the illustrative embodiments may be performed byprocessing unit 206 using computer implemented instructions, which maybe located in a memory, such as, for example, main memory 208, read onlymemory 224, or in one or more peripheral devices.

Furthermore, in one case, code 226A may be downloaded over network 201Afrom remote system 201B, where similar code 201C is stored on a storagedevice 201D. in another case, code 226A may be downloaded over network201A to remote system 201B, where downloaded code 201C is stored on astorage device 201D.

The hardware in FIGS. 1-2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1-2. In addition, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may comprise one or morebuses, such as a system bus, an I/O bus, and a PCI bus. Of course, thebus system may be implemented using any type of communications fabric orarchitecture that provides for a transfer of data between differentcomponents or devices attached to the fabric or architecture.

A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache, such as the cache found inNorth Bridge and memory controller hub 202. A processing unit mayinclude one or more processors or CPUs.

The depicted examples in FIGS. 1-2 and above-described examples are notmeant to imply architectural limitations. For example, data processingsystem 200 also may be a tablet computer, laptop computer, or telephonedevice in addition to taking the form of a mobile or wearable device.

Where a computer or data processing system is described as a virtualmachine, a virtual device, or a virtual component, the virtual machine,virtual device, or the virtual component operates in the manner of dataprocessing system 200 using virtualized manifestation of some or allcomponents depicted in data processing system 200. For example, in avirtual machine, virtual device, or virtual component, processing unit206 is manifested as a virtualized instance of all or some number ofhardware processing units 206 available in a host data processingsystem, main memory 208 is manifested as a virtualized instance of allor some portion of main memory 208 that may be available in the hostdata processing system, and disk 226 is manifested as a virtualizedinstance of all or some portion of disk 226 that may be available in thehost data processing system. The host data processing system in suchcases is represented by data processing system 200.

With reference to FIG. 3 , this figure depicts a block diagram of anexample configuration for combined classical/quantum predictorevaluation in accordance with an illustrative embodiment. Application300 is an example of application 105 in FIG. 1 and executes partially inany of servers 104 and 106, clients 110, 112, and 114, and device 132,and partially in quantum computing device 148 in FIG. 1 .

Application 300 receives, as input, a set of data comprises a set ofattribute-value pairs, in which the attribute labels or describes thedata value, or a stream, or set of values in order, labelled with theattribute. Application also receives, as input, a set of outcome dataassociated with the input, for use in model training and predictorevaluation.

Classical feature ranking/modelling module 310 uses a classical datamodel executing on a classical processor to select and score a set ofdata attributes, or features, from input training data. Oneimplementation of module 310 selects features to score using a set ofheuristics. Another implementation of module 310 selects features toscore using a set of analytical methods. Another implementation ofmodule 310 selects features to score using an automated artificialintelligence tool, such as AutoAI. As well as feature selection, module310 fills in missing data for the selected features, scales feature datato a common scale, and performs other data cleanup for selected featuresand their data.

Module 310 selects a model type with which to analyze the input trainingdata with respect to a feature being scored. One implementation ofmodule 310 selects a model type based on a characteristic of the featurebeing scored, such as a binary classification type model or a regressiontype model. Module 310 recursively re-selects features and refines aselected model type and model weights and parameters until it satisfiesa completion criterion, such as optimizing accuracy, by obtaining a setof features that best predict a particular result. Module 310 uses aweight of a feature within the resulting model as the feature's score.Another implementation of module 310 selects and refines an ensemble ofmodel types, adjusts each model in the ensemble's weights and parametersuntil each model satisfies a completion criterion, and combines theresulting weights from the different models into one score for afeature. The implementation combines the resulting weights using anypresently known weight-combining technique. Another implementation ofmodule 310 scores features by multiplying a feature's weight by theabsolute value of the Pearsons correlation coefficient. Anotherimplementation of module 310 uses, instead of model weights, the outputof a model's explainer module, which produces a score corresponding toan importance of a feature in predicting a desired result. Anotherimplementation of module 310 uses a presently known random foresttechnique to fit a random forest to input training data and scoresfeatures according to data fit. Other implementation of module 310 useother presently known techniques for scoring predictors using aclassical processor.

Module 310 uses the feature scores to select a set of highest-scored, orbest, features. One implementation of module 310 selects all featureswith scores above a threshold value. Another implementation of module310 ranks features by their scores, then selects a predetermined numberof the top-ranked features, for example the top ten or fifteen features.Another implementation of module 310 uses another feature selectionmethod.

Quantum feature ranking/modelling module 320 uses a quantum data modelexecuting on a quantum processor to select and score another set of dataattributes, or features, from input training data. One implementation ofmodule 320 starts with the top-ranked classical features (the featuresscored using the classical processor) and performs an RFECV technique ona QSVM to select and score a set of quantum features. Anotherimplementation of module 320 uses the top-ranked classical features toperform a QUBO technique and executes a QAOA on a quantum processor toselect and score a set of quantum features. Both QSVM and QAOA includean explainer module, which produces a score corresponding to animportance of a feature in predicting a desired result. Oneimplementation of module 320 applies the top-ranked classical featuresas quadratic penalties to force QUBO to find a different set ofpredictors as input to QAOA. Other implementations of module 320 useother presently known techniques for scoring predictors using a quantumprocessor.

Module 320 uses the feature scores scored by a quantum processor (thequantum features) to select a set of highest-scored, or best, quantumfeatures. One implementation of module 320 selects all quantum featureswith scores above a threshold value. Another implementation of module320 ranks quantum features by their scores, then selects a predeterminednumber of the top-ranked quantum features, for example the top ten orfifteen features. Another implementation of module 320 uses anotherquantum feature selection method.

Valuation module 330 correlates the scored set of classical features andthe scored set of quantum features into a combined set of scoredfeatures. In particular, module 330 selects a set of features that arecommon to both the classical and quantum feature sets. Then, for afeature in the combined set, module 330 multiples the feature'sclassical score (the score output using the classical processor) withthe feature's quantum score (the score output using the quantumprocessor), including a scaling factor if necessary to normalize bothscores to a common scale.

Module 330 uses the combined set of scored features and input data tocalculate a valuation of a resource by using a product sum of featurevalue and weight. One implementation of module 330 uses the combined setof scored features and a single set of input data to predict a valuationof a resource or predict an output value. Another implementation ofmodule 330 uses the combined set of scored features and a stream ofinput data to generate a stream of valuations or predictions. Timeintervals of input and output streams need not be the same.

Explainability module 340 uses a natural language processing system, thecombined set of scored features, and the valuation or prediction toconstruct a natural language explanation of the valuation or predictionexpressed in natural language form.

With reference to FIG. 4 , this figure depicts a block diagram of anexample configuration for combined classical/quantum predictorevaluation in accordance with an illustrative embodiment. Module 310 isthe same as module 310 in FIG. 3 . In particular, FIG. 4 depicts moredetail of module 310 in FIG. 3 .

Classical feature selection module 410 uses a classical data modelexecuting on a classical processor to select a set of features, usingone or more of using a set of heuristics, a set of analytical methods,an automated artificial intelligence tool such as AutoAI. Classicalmodelling module 420 selects a model type with which to analyze theinput training data with respect to a feature being scored, or selectsan ensemble of model types. Modules 410 and 420 recursively refine modelparameters until a model or ensemble of models satisfy a completioncriterion, obtaining a set of features that best predict a particularresult.

With reference to FIG. 5 , this figure depicts a block diagram of anexample configuration for combined classical/quantum predictorevaluation in accordance with an illustrative embodiment. Module 320 isthe same as module 320 in FIG. 3 . In particular, FIG. 5 depicts moredetail of module 320 in FIG. 3 .

In one implementation of module 320, quantum feature selection module510 performs RFECV on a QSVM implemented by quantum modelling module 520to select and score a set of quantum features. In another implementationof module 320, module 510 uses the top-ranked classical features toperform a QUBO technique and executes a QAOA implemented by quantummodelling module 520 to select and score a set of quantum features. Oneimplementation of module 510 applies the top-ranked classical featuresas quadratic penalties to force QUBO to find a different set ofpredictors as input to module 520.

With reference to FIG. 6 , this figure depicts an example of combinedclassical/quantum predictor evaluation in accordance with anillustrative embodiment. The example can be executed using application300 in FIG. 3 , partially in any of servers 104 and 106, clients 110,112, and 114, and device 132, and partially in quantum computing device148 in FIG. 1 . Classical feature ranking/modelling module 310, quantumfeature ranking/modelling module 320, valuation module 330, andexplainability module 340 are the same as classical featureranking/modelling module 310, quantum feature ranking/modelling module320, valuation module 330, and explainability module 340 in FIG. 3 .

Module 310 receives, as input, input attribute/value data 610, and usesa classical processor to generate scored classical features 620. Module320 uses input attribute/value data 610, scored classical features 620and a quantum processer to generate scored quantum features 630. Module330 uses scored classical features 620 and scored quantum features 630to generate resource valuation 640. Module 340 uses scored classicalfeatures 620 and scored quantum features 630 to generate resourcevaluation explanation 650.

With reference to FIG. 7 , this figure depicts a flowchart of an exampleprocess for combined classical/quantum predictor evaluation inaccordance with an illustrative embodiment. Process 700 can beimplemented in application 300 in FIG. 3 .

In block 702, the application scores, using a classical modellingtechnique, a set of classical features. In block 704, the applicationscores, using a quantum modelling technique and the scored set ofclassical features, a set of quantum features. In block 706, theapplication correlates, forming a combined set of scored features, thescored set of classical features and the scored set of quantum features.In block 708, the application determines a valuation of a resource, andan explanation of the valuation, using the combined set of scoredfeatures. Then the application ends.

Referring now to FIG. 8 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-Ndepicted are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 9 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 8 ) is shown. It shouldbe understood in advance that the components, layers, and functionsdepicted are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and application selection based on cumulativevulnerability risk assessment 96.

Thus, a computer implemented method, system or apparatus, and computerprogram product are provided in the illustrative embodiments forcombined classical/quantum predictor evaluation and other relatedfeatures, functions, or operations. Where an embodiment or a portionthereof is described with respect to a type of device, the computerimplemented method, system or apparatus, the computer program product,or a portion thereof, are adapted or configured for use with a suitableand comparable manifestation of that type of device.

Where an embodiment is described as implemented in an application, thedelivery of the application in a Software as a Service (SaaS) model iscontemplated within the scope of the illustrative embodiments. In a SaaSmodel, the capability of the application implementing an embodiment isprovided to a user by executing the application in a cloudinfrastructure. The user can access the application using a variety ofclient devices through a thin client interface such as a web browser(e.g., web-based e-mail), or other light-weight client-applications. Theuser does not manage or control the underlying cloud infrastructureincluding the network, servers, operating systems, or the storage of thecloud infrastructure. In some cases, the user may not even manage orcontrol the capabilities of the SaaS application. In some other cases,the SaaS implementation of the application may permit a possibleexception of limited user-specific application configuration settings.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

What is claimed is:
 1. A computer-implemented method comprising:scoring, using a classical data model executing on a classicalprocessor, a set of classical features, a classical feature in the setof classical features comprising a first attribute of a resource, ascore of the classical feature comprising an evaluation of a utility ofthe classical feature in predicting a result involving the resource;scoring, using a quantum data model executing on a quantum processor andthe scored set of classical features, a set of quantum features, aquantum feature in the set of quantum features comprising a secondattribute of the resource, a score of the quantum feature comprising anevaluation of a utility of the quantum feature in predicting the result;correlating, forming a combined set of scored features, the scored setof classical features and the scored set of quantum features; andcalculating, using the combined set of scored features and a first setof input data of a resource, a valuation of the resource.
 2. Thecomputer-implemented method of claim 1, further comprising: selecting,for scoring using the scored set of classical features as quadraticpenalties input to a quadratic unconstrained binary optimization, theset of quantum features.
 3. The computer-implemented method of claim 1,further comprising: constructing, using a natural language processingsystem, the combined set of scored features, and the valuation, anatural language explanation of the valuation, the natural languageexplanation expressed in natural language form.
 4. Thecomputer-implemented method of claim 1, further comprising: configuring,according to the valuation, a computing system deployment, the computingsystem deployment including the resource.
 5. The computer-implementedmethod of claim 1, further comprising: predicting, using the combinedset of scored features and a stream of input data, a stream of outputvalues corresponding to the stream of input data.
 6. Thecomputer-implemented method of claim 5, further comprising:constructing, using a natural language processing system, the combinedset of scored features, and the predicted stream of output values, anatural language explanation of the stream of output values, the naturallanguage explanation expressed in natural language form.
 7. A computerprogram product for predictor evaluation, the computer program productcomprising: one or more computer readable storage media, and programinstructions collectively stored on the one or more computer readablestorage media, the stored program instructions comprising: programinstructions to score, using a classical data model executing on aclassical processor, a set of classical features, a classical feature inthe set of classical features comprising a first attribute of aresource, a score of the classical feature comprising an evaluation of autility of the classical feature in predicting a result involving theresource; program instructions to score, using a quantum data modelexecuting on a quantum processor and the scored set of classicalfeatures, a set of quantum features, a quantum feature in the set ofquantum features comprising a second attribute of the resource, a scoreof the quantum feature comprising an evaluation of a utility of thequantum feature in predicting the result; program instructions tocorrelate, forming a combined set of scored features, the scored set ofclassical features and the scored set of quantum features; and programinstructions to calculate, using the combined set of scored features anda first set of input data of a resource, a valuation of the resource. 8.The computer program product of claim 7, the stored program instructionsfurther comprising: program instructions to select, for scoring usingthe scored set of classical features as quadratic penalties input to aquadratic unconstrained binary optimization, the set of quantumfeatures.
 9. The computer program product of claim 7, the stored programinstructions further comprising: program instructions to construct,using a natural language processing system, the combined set of scoredfeatures, and the valuation, a natural language explanation of thevaluation, the natural language explanation expressed in naturallanguage form.
 10. The computer program product of claim 7, the storedprogram instructions further comprising: program instructions toconfigure, according to the valuation, a computing system deployment,the computing system deployment including the resource.
 11. The computerprogram product of claim 7, the stored program instructions furthercomprising: program instructions to predict, using the combined set ofscored features and a stream of input data, a stream of output valuescorresponding to the stream of input data.
 12. The computer programproduct of claim 11, the stored program instructions further comprising:program instructions to construct, using a natural language processingsystem, the combined set of scored features, and the predicted stream ofoutput values, a natural language explanation of the stream of outputvalues, the natural language explanation expressed in natural languageform.
 13. The computer program product of claim 7, wherein the storedprogram instructions are stored in the at least one of the one or morestorage media of a local data processing system, and wherein the storedprogram instructions are transferred over a network from a remote dataprocessing system.
 14. The computer program product of claim 7, whereinthe stored program instructions are stored in the at least one of theone or more storage media of a server data processing system, andwherein the stored program instructions are downloaded over a network toa remote data processing system for use in a computer readable storagedevice associated with the remote data processing system.
 15. Thecomputer program product of claim 7, wherein the computer programproduct is provided as a service in a cloud environment.
 16. A computersystem comprising one or more processors, one or more computer-readablememories, and one or more computer-readable storage devices, and programinstructions stored on at least one of the one or more storage devicesfor execution by at least one of the one or more processors via at leastone of the one or more memories, the stored program instructionscomprising: program instructions to score, using a classical data modelexecuting on a classical processor, a set of classical features, aclassical feature in the set of classical features comprising a firstattribute of a resource, a score of the classical feature comprising anevaluation of a utility of the classical feature in predicting a resultinvolving the resource; program instructions to score, using a quantumdata model executing on a quantum processor and the scored set ofclassical features, a set of quantum features, a quantum feature in theset of quantum features comprising a second attribute of the resource, ascore of the quantum feature comprising an evaluation of a utility ofthe quantum feature in predicting the result; program instructions tocorrelate, forming a combined set of scored features, the scored set ofclassical features and the scored set of quantum features; and programinstructions to calculate, using the combined set of scored features anda first set of input data of a resource, a valuation of the resource.17. The computer system of claim 16, the stored program instructionsfurther comprising: program instructions to select, for scoring usingthe scored set of classical features as quadratic penalties input to aquadratic unconstrained binary optimization, the set of quantumfeatures.
 18. The computer system of claim 16, the stored programinstructions further comprising: program instructions to construct,using a natural language processing system, the combined set of scoredfeatures, and the valuation, a natural language explanation of thevaluation, the natural language explanation expressed in naturallanguage form.
 19. The computer system of claim 16, the stored programinstructions further comprising: program instructions to configure,according to the valuation, a computing system deployment, the computingsystem deployment including the resource.
 20. The computer system ofclaim 16, the stored program instructions further comprising: programinstructions to predict, using the combined set of scored features and astream of input data, a stream of output values corresponding to thestream of input data.